Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome1 Getting Data for Business Statistics: A Response...

Thursday, 10 July, 2008 Q2008, 8-11 July 2008, Rome 1

Getting Data for Business Statistics:

A Response Model for Business Surveys

Ger Snijkers

Statistics NetherlandsUtrecht University


Getting Data for Business Statistics

How do we get the data we needfor business statistics?

Yesterday, today, tomorrow

Data• In time• Complete• Correct

Statistical picture of a country

NSI

Survey Parameters

in and out of control

Respondent

Parameters


Getting Data for Business Statistics

Over the years:

1. The day before yesterday: ICES-I* 1993

2. Yesterday: ICES-II 2000

3. Today: ICES-III 2007

4. Tomorrow

* International Conference on Establishment Surveys


Getting Data for Business Statistics The day before yesterday

ICES-I (1993):

1. Surveying various branches of industry:agriculture, energy, health care, trade, finance, education, manufacturing industry

2. Quality of business frames & sampling

3. Data analysis & Estimation

4. Data collection methodology:data quality, registers, non-response, Q-design

‘Stove-pipe’ approach The ‘one-size-fits-all’ survey design


Response• In time• Complete• Correct

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Motivation

Respondentburden

• De facto • Perception

Internal business factors• Policy• Data• Resources• Market position

Informant:• Mandate• Data knowledge• Job priority

External business factors• Econ. climate• Regulatory requirements• Political climate

The survey:• Topic• Population and sample• Sponsor / Survey organisation• Resources• Planning• Authority/confidentiality

The survey design

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Black box

A business

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Survey designs not coordinated:• ‘Stove-pipe’ approach

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‘One-size-fits-all’


Getting Data for Business Statistics Yesterday

ICES-II (2000):• Issues in government surveys• Data collection modes & non-response• The response process• Use of register data• Sampling• Editing and Data Quality• Data analysis, estimation and

dissemination


Getting Data for Business Statistics Today

ICES-III (2007):

1. Survey data collection methodology:• questionnaire design & pre-testing • survey participation: non-response reduction,

response burden, bias • mixed-mode designs & e-data collection• understanding the response process in bus’s

2. Using administrative data

3. Business frames & Sampling

4. Weighting, Outlier detection, Estimation & Data analysis



International Workshop on Business DataCollection Methodology

1. London, 2006: ONS2. Ottawa, 2008: Statistics Canada

• Organising Committee:• Ger Snijkers (Stats Netherlands) • Gustav Haraldsen (Stats Norway) • Jacqui Jones (ONS)• Diane Willimack (US Census Bureau)

• Practices, developments, research issues



International Workshop on Business DataCollection Methodology

1. Primary data collection: • questionnaire design & pre-testing

• survey participation: non-response reduction, response burden & bias, contact strategies

• mixed-mode designs & e-data collection• understanding the response process in bus’s

2. Secondary data collection: • use of registers

3. Multi-source designs: • combining survey and administrative data


Getting Data for Business Statistics Over the years

General picture:• 1993:

• 2007:

• ‘Stove-pipe’ approach• One-size-fits-all • Survey organisation is central

• Systematisation andstandardisation of methods

• Mixed-mode, multi-source• Respondent is central: tailoring

• 2000: • Transition


Getting Data for Business StatisticsThe data collection design today

Challenge:• Good statistics:

• relevant• more & integrated information• faster

• Less money• Less compliance costs:

• providing data only once to government

Consequences for the data collection …


Getting Data for Business Statistics Consequences for data collection

1. Using more and more register data:• Definitions of variables

• Definitions of units

• Timeliness of register

• Quality of register data

• Combining register and survey data○ Managing integrated sets of statistics using various

data sourcesNot: Managing stove-pipes (a survey and relatedstatistics)



2. Additional data collection:• When register data are not available:

○ Not in time○ Additional information needed:

- variables - target population

○ Quality is not good



3. Sample design:• Controlling for overlap across surveys• Controlling for rotation over time

To avoid this:



4. Survey design:• Mode of data collection:

○ EDI: XBRL○ Mixed-mode designs:

Internet, paper, telephone (CATI)

• Questionnaire design:○ Tailored to information bus’s have in their records○ Controlling for overlap across questionnaires○ Pre-tested for Q-A process and usability

• Contact strategy:○ When data are available (not when we need them)○ Motivating and stimulating respondents:

- Compliance principles → To avoid these reactions:



Reactions by businesses:• “What is the use of this survey?”

“It is pointless!”• “There is no connection with my business

activities.”• “It only costs money and time!”

“The costs outweigh the added value.”“There is no added value.”

• “Pick someone else. Although you say it is a sample, I am in it every time.”


Getting Data for Business Statistics The data collection design today

More complex than yesterday:

• More data sources• Dependent on providers of registers

• Mixed-mode designs• Coordinated development over modes• Tailored to mode

• Tailoring to subgroups• Tailored to target populations

- opening the black box: the response process• Coordinated over surveys (ask only once)

Tailored multi-source/mixed-mode design



De

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Motivation

Respondentburden


• More than one survey• More than once• In other ways: ○ Registers ○ EDI




Image


The survey design

Co

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NSI NSI

Black box

A business


Getting Data for Business Statistics The data collection design today

Tailored multi-source/mixed-mode design:• Small businesses:

• register data (+ survey data)

• Middle-sized businesses:• register data + survey data

• Large businesses:• consistent data collection for:

- all businesses- all variables

It is our job to make statistics out of these data


Getting Data for Business Statistics Tomorrow

Improving thetailored multi-source/mixed-mode design• Advanced statistical modelling:

• Estimations based on multiple sources andmixed-mode surveys

• Managing integrated sets of statistics (not stove-pipes)• Opening the businesses:

• Insight in the response process • Tailored surveys to the internal business’s processes

• Opening the survey process:• Improved relationships with businesses:

- What surveys, when, feedback, involving bus’s• Systematisation and standardisation of survey designs:

- Survey parameters in control



De

cis

ion

to p

articip

ate

An

sw

erin

g b

eh

av

iou

r

Motivation

Respondentburden


• More than one survey• More than once• In other ways: ○ Registers ○ EDI




Image


The survey design

Co

nta

cts

trateg

y

Qu

es

tion

naire

Mo

de

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Statistical picture of a country

NSI NSIA business

Registerdata


References

American Statistical Association, Proceedings of ICES-I (1993), ICES-II (2000) and ICES-III (2007). Alexandria (Virginia).

Groves, R.M., and M.P. Couper (1998), Nonresponse in Household Interview Surveys. Wiley, New York.

Hedlin, D., T. Dale, G. Haraldsen, and J. Jones (2005), Developing Methods for Assessing Perceived Response Burden. Statistics Sweden, Stockholm, Statistics Norway, Oslo, and UK Office for National Statistics, London.

Snijkers, G. (2007), Between Chaos and Creation. Inaugural lecture Utrecht University (in Dutch). Statistics Netherlands, Heerlen.

Snijkers, G. (2007), Collecting Data for Business Statistics: Yesterday, Today, Tomorrow. Presentation at 56th Meeting of the ISI, 22-29 August 2007, Lisbon, Portugal.

Snijkers, G. (2007), Collecting Data for Business Statistics: A Response Model. Proceedings of the 56th Meeting of the ISI (CD-rom), 22-29 August 2007, Lisbon, Portugal.

Willimack, D.K., E. Nichols, and S. Sudman (2002), Understanding Unit and Item Nonresponse in Business Surveys. In: Groves, R., D. Dillman, J. Eltinge, and R. Little (eds.), Survey Nonresponse, pp. 213-227. Wiley, New York.

Thursday, 10 July, 2008Q2008, 8-11 July 2008, Rome1 Getting Data for Business Statistics: A Response...

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